Histological and hematological analysis of patient specimens on prepared slides remains a cornerstone of clinical medicine. Such processes, including specimen preparation as well as analysis, are becoming increasingly automated. Automated histology and hematology increase specimen throughput while decreasing the incidence of observer bias and the influence of human subjectivity on the preparation and analysis processes.
With the advancement of computers, data processing and graphic software, along with the progress in artificial neural networks, digital image based hematology systems have become routine instruments in clinical laboratories. An image based hematology system typically works in part as a cell locator providing pre-classification of cells for further verification of cells' categories and detected cellular abnormalities by a skilled operator.
By nature of the processes used to smear hematological specimens, such as blood, across a slide for analysis (e.g., to prepare a blood film on the microscopy slide), regardless of whether the smear is produced by an automated device or a human technician, the smear will include areas that are less than optimal or even insufficient for proper analysis. In addition, the smear (or blood film) shall also include a certain type of cellular distribution commonly known as a feather edge used for evaluation of certain types of blood cell parameters. The accuracy and efficiency of analysis of hematological blood smear specimens is greatly increased by identification of areas of the slide where the smear is of proper thickness for analysis and has a proper density of cell distribution, i.e., not overly dense or thick and not overly diffuse or thin. Accordingly, the automated analysis of blood smears can be improved through automated identification of areas of the slide suitable for analysis and determination of which area(s) display optimal cell dispersion characteristics for a particular analysis protocol.
Furthermore, the automation of histological specimen analysis allows for the high throughput of many specimens by a single histology laboratory. Such high rates of processing place particular importance on highly accurate and efficient specimen tracking. However, inconsistent labeling of specimen slides, e.g., as is common in clinical pathology labs present in busy environments such as large hospitals, introduces constraints on the methods available for specimen tracking. In some cases, slides may be hand labeled or annotated. Hand labeling is often not readable by devices used to extract specimen identifying information during automated analysis. In addition, hand labeling can obscure machine-readable labels rendering tracking of samples difficult. While re-labeling or overwriting hand written labels with machine-readable labels may circumvent some of these issues, doing so can discard important information provided in the hand label or introduce specimen tracking error where an incorrect label is applied during the re-labeling process.
Improvements in the identification of specimen analysis areas and in specimen information tracking promise to further advance automated processing of histological specimens and the overall functioning of automated histology and hematological analyzing systems.